If the gradient step in A2C is too large, the policy may astray which in turn will produce bad samples causing divergence in the solution.
As a result, we have to select very small step size which slows the learning process. [1]} introduced PPO algorithm that make sure the gradient steps are small enough to make the algorithm data efficient.
This is done by formulating the network objective function in terms of a ratio of two policies (old and new) using principle of importance sampling.
Appropriate gradient steps are chosen by clipping the ratio of old and new policy within the range \([1-\epsilon ,1+\epsilon ]\) , where \(\epsilon \)  is generally a small fractional number.
The modified objective function for policy network is defined as,
\(J_{ppo}(\theta ) = \hat{\mathbb {E}}_t \left[ \min ( r_t(\theta ) \hat{A}_t (s_t,a_t),\ \textup {clip}(r_t(\theta ),1-\epsilon ,1+\epsilon )\hat{A}_t (s_t,a_t) ) \right],\) 
